深層モデル融合強化学習による効率的なロボットタスクの汎化
Efficient Robotic Task Generalization Using Deep Model Fusion Reinforcement Learning
学習済みモデルを融合して環境適応を効率化するDeep Model Fusion強化学習と、学習効率を高める多目的誘導報酬整形を提案し、様々な環境で有効性を検証した論文。
著者: Tianying Wang, Hao Zhang, Wei Qi Toh, Hongyuan Zhu, Cheston Tan, Yan Wu, Yong Liu, Wei Jing
分類: cs.RO, cs.AI
原文アブストラクト
Learning-based methods have been used to pro-gram robotic tasks in recent years. However, extensive training is usually required not only for the initial task learning but also for generalizing the learned model to the same task but in different environments. In this paper, we propose a novel Deep Reinforcement Learning algorithm for efficient task generalization and environment adaptation in the robotic task learning problem. The proposed method is able to efficiently generalize the previously learned task by model fusion to solve the environment adaptation problem. The proposed Deep Model Fusion (DMF) method reuses and combines the previously trained model to improve the learning efficiency and results.Besides, we also introduce a Multi-objective Guided Reward(MGR) shaping technique to further improve training efficiency.The proposed method was benchmarked with previous methods in various environments to validate its effectiveness.
